A Multi-Agent System framework to support the decision-making in complex real-world domains

The aim of this work was to develop a framework capable of supporting the decision-making process in complex real-world domains, such as environmental, industrial or medical domains using a Multi-Agent approach with Rule-based Reasoning. The validation of the framework was done in the environmental domain, particularly in the area of river basins

Incorporating the elements of the mase methodology into agent open

Enterprise-wide, web-based systems can be assisted in their construction by the use of agents and an agent-oriented methodology. As part of an extensive research programme to create such an AO methodology by combining the benefits of method engineering and existing object-oriented frameworks (notably the OPF), we have analysed here contributions to the OPF repository of process components from the MASE agent-oriented methodology, identifying three new Tasks, one additional Technique and two new Work Products

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Simulating Interactive Learning Scenarios with Intelligent Pedagogical Agents in a Virtual World through BDI-Based Agents

Intelligent Pedagogical Agents (IPAs) are designed for pedagogical purposes to support learning in 3D virtual learning environments. Several benefits of IPAs have been found adding to support learning effectiveness. Pedagogical agents can be thought of as a central point of interaction between the learner and the learning environment. And hence, the intelligent behavior and functional richness of pedagogical agents have the potential to reward back into increased engagement and learning effectiveness. However, the realization of those agents remains to be a challenge based on intelligent agents in virtual worlds. This paper reports the challenging reasons and most importantly an approach for simplification. A simulation based on BDI agents is introduced opening the road for several extensions and experimentation before implementation of IPAs in a virtual world can take place. The simulation provides a proof-of concept based on three intelligent agents to represent an IPA, a learner, and learning object implemented in JACK and Jadex intelligent agent platforms. To that end, the paper exhibits the difficulties, resolutions, and decisions made when designing and implementing the learning scenario in both domains of the virtual world and the agent-based simulation while comparing the two agent platforms

Internet collaboration and service composition as a loose form of teamwork

This paper describes Web service composition as a form of teamwork, where the Web services are team members in a loose collaboration. We argue that newer hierarchical teamwork models are more appropriate for Web service composition than the traditional models involving joint beliefs and joint intentions. We describe our system for developing and executing Web service compositions as team plans in JACK Teams,((TM) 1) and discuss the relationships between this approach and service orchestration languages such as Business Process Execution Language for Web Services (BPEL4WS). We discuss briefly how the use of Al planning can also be incorporated into this model, and identify some of the research issues involved. Incorporating Web service compositions into a mature Belief Desire Intention (BDI) agent team framework allows for integration of Web services seamlessly into a powerful application execution paradigm that supports sophisticated reasoning

Agent oriented programming: An overview of the framework and summary of recent research

This is a short overview of the agent-oriented programming (AOP) framework. AOP can be viewed as an specialization of object-oriented programming. The state of an agent consists of components called beliefs, choices, capabilities, commitments, and possibly others; for this reason the state of an agent is called its mental state. The mental state of agents is captured formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for commitment, choice and capability. Agents are controlled by agent programs, which include primitives for communicating with other agents. In the spirit of speech-act theory, each communication primitive is of a certain type: informing, requesting, offering, etc. This document describes these features in more detail and summarizes recent results and ongoing AOP-related work

A BDI agent programming language with failure handling, declarative goals, and planning

Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The Belief- Desire-Intention (BDI) agent paradigm has proven to be one of the major approaches to intelligent agent systems, both in academia and in industry. Typical BDI agent-oriented programming languages rely on user-provided ''plan libraries'' to achieve goals, and online context sensitive subgoal selection and expansion. These allow for the development of systems that are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with (soft) real-time reasoning and control requirements. Nonetheless, complex decision making that goes beyond, but is compatible with, run-time context-dependent plan selection is one of the most natural and important next steps within this technology. In this paper we develop a typical BDI-style agent-oriented programming language that enhances usual BDI programming style with three distinguished features: declarative goals, look-ahead planning, and failure handling. First, an account that mixes both procedural and declarative aspects of goals is necessary in order to reason about important properties of goals and to decouple plans from what these plans are meant to achieve. Second, lookahead deliberation about the effects of one choice of expansion over another is clearly desirable or even mandatory in many circumstances so as to guarantee goal achievability and to avoid undesired situations. Finally, a failure handling mechanism, suitably integrated with both declarative goals and planning, is required in order to model an adequate level of commitment to goals, as well as to be consistent with most real BDI implemented systems

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis